Tag: institutional knowledge

Structured Content: The Missing Piece in AI Architecture

Structured Content: The Missing Piece in AI Architecture

Debra W Kahn (@debrawk.bsky.social)

You can’t scroll through LinkedIn these days without encountering elaborate diagrams of AI architecture. They are everywhere. With spooky colors and black backgrounds, they illustrate how LLMs, retrieval systems, AI agents, orchestration tools, and governance mechanisms work together to deliver business results.

I’ve been looking at a number of these models recently. Some are remarkably detailed, with layers devoted to everything from data management to security and human oversight. But something stands out: Many acknowledge the importance of a clean enterprise data and knowledge layer, yet few explicitly address structured content or the professionals who manage it.

That’s a consequential omission. For decades, technical communicators, content strategists, and information architects have developed methods to organize, classify, maintain, and reuse information. These methods have renewed relevance as organizations build trusted AI systems that depend on reliable organizational knowledge.

Overlook that relevance at your own risk.

Structured Content: Established Principles, New Relevance for AI

Structured content has been extensively defined, described, and standardized by international bodies and leading institutions. It is information that is organized into modular, discrete, self-contained components that are:

  • Consistently architected for human and machine readability
  • Identifiable
  • Semantically tagged (without regard to formatting)
  • Version-controlled
  • Reusable
  • Made relative and relevant with component-level metadata
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Don’t Let Your AI Proof of Concept End With the Demo

Don’t Let Your AI Proof of Concept End With the Demo

Debra W Kahn (@debrawk.bsky.social)

In a September 24 letter to The Wall Street Journal, Sparq CEO Ingrid Curtis makes an important distinction between experimenting with AI and being ready to deploy it enterprise-wide. “A successful pilot proves little,” she writes. AI earns trust when it performs reliably “within the messy reality of a working business.”

One widely circulated 2025 statistic, based on IDC research for Lenovo (and reported by CIO), suggested that 88% of AI proofs of concept (PoCs) fail to result in widespread deployment. BCG reported in late 2024 that 74% of companies struggle to achieve and scale value from AI. But deployment may not be the only measure of a successful PoC. What did the organization learn from the experiment, and what happened to that knowledge?

Curtis argues that companies need to better prepare their AI applications, data, infrastructure, systems, and employees for that reality. I would add that the two most important contributors to this preparation are documentation and communication.

In my last two posts, I’ve focused primarily on external content—and the AI-mitigated audiences that interact with it. But demand for enterprise-ready AI is also creating demand for important internal content: the knowledge organizations generate as they experiment with the technology.

That knowledge shouldn’t disappear when the PoC ends.

An internal AI PoC is not complete when an organization determines whether the technology can perform as expected. It is complete when the organization documents, communicates, and applies what it learned to the decisions that come next.

Documentation creates organizational memory. Communication creates organizational knowledge. Both are essential if an AI PoC is going to become more than a successful experiment.

In this blog post, I answer these questions:

  • What is an AI PoC?
  • How can AI PoC documentation capture knowledge?
  • How can communication about AI PoC findings serve future efforts?
  • What kind of organizational knowledge can an AI PoC yield?
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